School of Information & Library Science
305 Manning Hall
University of North Carolina at Chapel Hill
Chapel Hill, NC 27599-3360

III: Medium: Collaborative Research: Athena: Learning-oriented Search with Personalized Learning Flows

Project Summary

The Athena project will develop technology called "search as learning," a set of search technologies that encourage and support learning rather than just simple document finding. In order to learn, searchers must engage with information that is both novel and understandable. Therefore, at the core, Athena will support learning by modeling several important factors: (1) the knowledge connections between documents covering a topic, (2) a user's current state of knowledge on that topic, (3) the types of knowledge a user is likely to gain from a document, and (4) the knowledge required for a user to successfully engage with a document. The Athena project will involve two types of end-to-end systems, both of which will model and leverage the learner's state of knowledge (LSK): an LSK-aware search engine and an LSK-aware question answering system. The Athena systems will guide a user through a topic and find relevant information in the context of previously encountered information and the topic structure captured in a web of topics. The team will evaluate Athena using standard measures as well as a series of studies involving human subjects. If the Athena project is successful, it will make it easier for people to use search engines and related technologies to learn about complex topics, where there are numerous interrelated and dependent subtopics that should be considered. Given that search is among the most common online activities on and off the Web, Athena and its technologies will have a substantial impact on searchers trying to learn about topics.

Athena enables "search as learning" using a data structure referred to as a Learning Flow Graph (LFG). An LFG comprises nodes that represent sub-topics (e.g., concepts) within a given domain and vertices that represent relations between sub-topics (e.g., one sub-topic being foundational to understand another). Athena leverages LFGs to model the different factors mentioned above. It uses probability distributions across nodes in an LFG to model: (1) a user's knowledge state, (2) the potential knowledge gains from an information item, and (3) the prerequisite knowledge required for a user to successfully engage with an information item. The Athena team will develop algorithms for generating LFGs from structured and semi- and unstructured resources (e.g., course syllabi, tables of contents, book indices, knowledge bases, query logs), algorithms for integrating LFGs into search and question-answering models, and algorithms for re-estimating LFGs and a user's knowledge state based on search behaviors (e.g., queries, clicks, skips, dwell times, etc.).

UNC Project Personnel

Dissemination of Research Results

Kelsey Urgo. Investigating the Influence of Subgoals on Learning during Search. Doctoral Dissertation. University of North Carolina at Chapel Hill. 2023.

Kelsey Urgo and Jaime Arguello. Capturing Self-Regulated Learning During Search. Proceedings of the 3rd International Workshop on Investigating Learning During Web Search (IWILDS'22), 2022.

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This research is sponsored by National Science Foundation grant IIS-2106334. Any opinions, findings, conclusions or recommendations expressed on this Web site are those of the author(s), and do not necessarily reflect those of the sponsor.